Overview

Dataset statistics

Number of variables13
Number of observations1272
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.3 KiB
Average record size in memory104.1 B

Variable types

Numeric12
DateTime1

Alerts

gpu_memory_usage has constant value "2296381440.0" Constant
gpu_load has constant value "100.0" Constant
unpaid has constant value "0" Constant
reported_hashrate has constant value "3200000.0" Constant
df_index is highly correlated with relative_hourHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
df_index is uniformly distributed Uniform
relative_hour is uniformly distributed Uniform
df_index has unique values Unique
ts has unique values Unique
relative_hour has unique values Unique
hashrate has 174 (13.7%) zeros Zeros
unpaid has 1272 (100.0%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:31:54.405834
Analysis finished2021-12-01 03:32:13.002826
Duration18.6 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean820.5
Minimum185
Maximum1456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:13.068810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum185
5-th percentile248.55
Q1502.75
median820.5
Q31138.25
95-th percentile1392.45
Maximum1456
Range1271
Interquartile range (IQR)635.5

Descriptive statistics

Standard deviation367.3390804
Coefficient of variation (CV)0.4477014996
Kurtosis-1.2
Mean820.5
Median Absolute Deviation (MAD)318
Skewness0
Sum1043676
Variance134938
MonotonicityStrictly increasing
2021-11-30T22:32:13.206935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1851
 
0.1%
10301
 
0.1%
10371
 
0.1%
10361
 
0.1%
10351
 
0.1%
10341
 
0.1%
10331
 
0.1%
10321
 
0.1%
10311
 
0.1%
10291
 
0.1%
Other values (1262)1262
99.2%
ValueCountFrequency (%)
1851
0.1%
1861
0.1%
1871
0.1%
1881
0.1%
1891
0.1%
ValueCountFrequency (%)
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%

ts
Date

UNIQUE

Distinct1272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Minimum2021-11-11 09:12:54-05:00
Maximum2021-11-11 12:53:28-05:00
2021-11-30T22:32:13.338377image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:13.517188image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

Distinct32
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3483490566
Minimum0.1
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:13.650947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.2
median0.2
Q30.3
95-th percentile0.6
Maximum16.2
Range16.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.7922800871
Coefficient of variation (CV)2.274385626
Kurtosis247.4167372
Mean0.3483490566
Median Absolute Deviation (MAD)0.1
Skewness14.3285248
Sum443.1
Variance0.6277077364
MonotonicityNot monotonic
2021-11-30T22:32:13.765102image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.2606
47.6%
0.3471
37.0%
0.459
 
4.6%
0.141
 
3.2%
0.523
 
1.8%
0.618
 
1.4%
0.714
 
1.1%
0.85
 
0.4%
14
 
0.3%
1.24
 
0.3%
Other values (22)27
 
2.1%
ValueCountFrequency (%)
0.141
 
3.2%
0.2606
47.6%
0.3471
37.0%
0.459
 
4.6%
0.523
 
1.8%
ValueCountFrequency (%)
16.21
0.1%
15.31
0.1%
9.41
0.1%
7.21
0.1%
5.71
0.1%

cpu_freq
Real number (ℝ≥0)

Distinct1236
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean957.9095833
Minimum802.77
Maximum3670.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:13.901571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum802.77
5-th percentile821.9075
Q1852.0825
median887.985
Q3942.86
95-th percentile1211.4565
Maximum3670.62
Range2867.85
Interquartile range (IQR)90.7775

Descriptive statistics

Standard deviation301.0303938
Coefficient of variation (CV)0.3142576283
Kurtosis43.36028687
Mean957.9095833
Median Absolute Deviation (MAD)41.255
Skewness6.091893073
Sum1218460.99
Variance90619.298
MonotonicityNot monotonic
2021-11-30T22:32:14.148690image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851.032
 
0.2%
870.572
 
0.2%
857.462
 
0.2%
998.392
 
0.2%
1002.152
 
0.2%
867.812
 
0.2%
867.222
 
0.2%
1097.892
 
0.2%
882.992
 
0.2%
861.292
 
0.2%
Other values (1226)1252
98.4%
ValueCountFrequency (%)
802.771
0.1%
806.141
0.1%
807.91
0.1%
808.221
0.1%
808.271
0.1%
ValueCountFrequency (%)
3670.621
0.1%
3646.421
0.1%
3643.491
0.1%
3593.781
0.1%
3593.561
0.1%

memory_usage
Real number (ℝ≥0)

Distinct1124
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1861350902
Minimum1448673280
Maximum2159689728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:14.283325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1448673280
5-th percentile1453471130
Q11968330752
median1980700672
Q31985700864
95-th percentile1988671898
Maximum2159689728
Range711016448
Interquartile range (IQR)17370112

Descriptive statistics

Standard deviation220150494.5
Coefficient of variation (CV)0.1182745791
Kurtosis-0.2974227838
Mean1861350902
Median Absolute Deviation (MAD)6105088
Skewness-1.294418044
Sum2.367638348 × 1012
Variance4.846624023 × 1016
MonotonicityNot monotonic
2021-11-30T22:32:14.417289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19768360963
 
0.2%
14563041283
 
0.2%
19866542083
 
0.2%
19858882563
 
0.2%
14560747523
 
0.2%
14565539843
 
0.2%
14553333763
 
0.2%
19868835843
 
0.2%
19793633283
 
0.2%
19873914882
 
0.2%
Other values (1114)1243
97.7%
ValueCountFrequency (%)
14486732801
0.1%
14488043521
0.1%
14489968641
0.1%
14491361281
0.1%
14492016641
0.1%
ValueCountFrequency (%)
21596897281
0.1%
20800552961
0.1%
20764221441
0.1%
20265574401
0.1%
19982909441
0.1%

cpu_temp
Real number (ℝ≥0)

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.24292453
Minimum26
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:14.533746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile30
Q132
median32
Q333
95-th percentile34
Maximum43
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.219412971
Coefficient of variation (CV)0.03781955232
Kurtosis9.627640412
Mean32.24292453
Median Absolute Deviation (MAD)1
Skewness-0.7694822866
Sum41013
Variance1.486967994
MonotonicityNot monotonic
2021-11-30T22:32:14.621296image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32537
42.2%
33447
35.1%
31127
 
10.0%
3478
 
6.1%
3030
 
2.4%
2917
 
1.3%
2711
 
0.9%
289
 
0.7%
359
 
0.7%
263
 
0.2%
Other values (4)4
 
0.3%
ValueCountFrequency (%)
263
 
0.2%
2711
 
0.9%
289
 
0.7%
2917
1.3%
3030
2.4%
ValueCountFrequency (%)
431
 
0.1%
381
 
0.1%
371
 
0.1%
361
 
0.1%
359
0.7%

gpu_memory_usage
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2296381440
Minimum2296381440
Maximum2296381440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:14.715501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2296381440
5-th percentile2296381440
Q12296381440
median2296381440
Q32296381440
95-th percentile2296381440
Maximum2296381440
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2296381440
Median Absolute Deviation (MAD)0
Skewness0
Sum2.920997192 × 1012
Variance0
MonotonicityIncreasing
2021-11-30T22:32:14.796567image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
22963814401272
100.0%
ValueCountFrequency (%)
22963814401272
100.0%
ValueCountFrequency (%)
22963814401272
100.0%

gpu_load
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100
Minimum100
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:14.881961image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean100
Median Absolute Deviation (MAD)0
Skewness0
Sum127200
Variance0
MonotonicityIncreasing
2021-11-30T22:32:14.961032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1001272
100.0%
ValueCountFrequency (%)
1001272
100.0%
ValueCountFrequency (%)
1001272
100.0%

gpu_temp
Real number (ℝ≥0)

Distinct39
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.25471698
Minimum36
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:15.064304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile62
Q163
median65
Q367
95-th percentile71
Maximum78
Range42
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.655231499
Coefficient of variation (CV)0.05601482418
Kurtosis11.73859349
Mean65.25471698
Median Absolute Deviation (MAD)2
Skewness-1.45419031
Sum83004
Variance13.36071731
MonotonicityNot monotonic
2021-11-30T22:32:15.177846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
64271
21.3%
63246
19.3%
65183
14.4%
66128
10.1%
67109
8.6%
6867
 
5.3%
6950
 
3.9%
7142
 
3.3%
6241
 
3.2%
7039
 
3.1%
Other values (29)96
 
7.5%
ValueCountFrequency (%)
361
0.1%
411
0.1%
422
0.2%
431
0.1%
441
0.1%
ValueCountFrequency (%)
781
 
0.1%
772
 
0.2%
762
 
0.2%
758
0.6%
7411
0.9%

hashrate
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2269392.031
Minimum0
Maximum4444444.44
Zeros174
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:15.275253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12222222.22
median2222222.22
Q33333333.33
95-th percentile3333333.33
Maximum4444444.44
Range4444444.44
Interquartile range (IQR)1111111.11

Descriptive statistics

Standard deviation1184198.014
Coefficient of variation (CV)0.5218128897
Kurtosis-0.3256136988
Mean2269392.031
Median Absolute Deviation (MAD)1111111.11
Skewness-0.5298384219
Sum2886666664
Variance1.402324935 × 1012
MonotonicityNot monotonic
2021-11-30T22:32:15.353192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2222222.22528
41.5%
3333333.33396
31.1%
0174
 
13.7%
1111111.11114
 
9.0%
4444444.4460
 
4.7%
ValueCountFrequency (%)
0174
 
13.7%
1111111.11114
 
9.0%
2222222.22528
41.5%
3333333.33396
31.1%
4444444.4460
 
4.7%
ValueCountFrequency (%)
4444444.4460
 
4.7%
3333333.33396
31.1%
2222222.22528
41.5%
1111111.11114
 
9.0%
0174
 
13.7%

unpaid
Real number (ℝ≥0)

CONSTANT
REJECTED
ZEROS

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros1272
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:15.440993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2021-11-30T22:32:15.515227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
01272
100.0%
ValueCountFrequency (%)
01272
100.0%
ValueCountFrequency (%)
01272
100.0%

reported_hashrate
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3200000
Minimum3200000
Maximum3200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:15.594735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3200000
5-th percentile3200000
Q13200000
median3200000
Q33200000
95-th percentile3200000
Maximum3200000
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean3200000
Median Absolute Deviation (MAD)0
Skewness0
Sum4070400000
Variance0
MonotonicityIncreasing
2021-11-30T22:32:15.674744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
32000001272
100.0%
ValueCountFrequency (%)
32000001272
100.0%
ValueCountFrequency (%)
32000001272
100.0%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.374694051
Minimum0.5355555556
Maximum4.211666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2021-11-30T22:32:15.809132image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5355555556
5-th percentile0.7195833333
Q11.455694444
median2.375138889
Q33.294097222
95-th percentile4.028194444
Maximum4.211666667
Range3.676111111
Interquartile range (IQR)1.838402778

Descriptive statistics

Standard deviation1.062526675
Coefficient of variation (CV)0.4474372919
Kurtosis-1.200170445
Mean2.374694051
Median Absolute Deviation (MAD)0.92
Skewness-0.001265427289
Sum3020.610833
Variance1.128962936
MonotonicityStrictly increasing
2021-11-30T22:32:15.962084image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53555555561
 
0.1%
2.9811111111
 
0.1%
3.0013888891
 
0.1%
2.9986111111
 
0.1%
2.9958333331
 
0.1%
2.9927777781
 
0.1%
2.991
 
0.1%
2.9869444441
 
0.1%
2.9841666671
 
0.1%
2.9783333331
 
0.1%
Other values (1262)1262
99.2%
ValueCountFrequency (%)
0.53555555561
0.1%
0.53833333331
0.1%
0.54138888891
0.1%
0.54416666671
0.1%
0.54694444441
0.1%
ValueCountFrequency (%)
4.2116666671
0.1%
4.2088888891
0.1%
4.2058333331
0.1%
4.2030555561
0.1%
4.21
0.1%

Interactions

2021-11-30T22:32:11.090426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:54.644483image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:56.124349image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:57.903369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:59.288409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:00.646209image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:02.068727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:03.439836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:05.028826image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:06.674998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:08.078292image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:09.664628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:11.227002image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:54.758428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:56.268628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:58.012065image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:59.400836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:00.758171image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:02.175910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:03.564263image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:05.143012image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:06.813598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:08.193028image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2021-11-30T22:32:16.178904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:32:16.382670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:32:16.558723image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:32:16.731158image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T22:32:12.712452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T22:32:12.930403image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
01852021-11-11 09:12:54-05:001.0886.36145272422426.02.296381e+09100.036.02222222.2203200000.00.535556
11862021-11-11 09:13:04-05:000.2814.37145339187227.02.296381e+09100.042.02222222.2203200000.00.538333
21872021-11-11 09:13:15-05:000.3825.45145343283226.02.296381e+09100.041.02222222.2203200000.00.541389
31882021-11-11 09:13:25-05:000.2871.46145393254426.02.296381e+09100.042.02222222.2203200000.00.544167
41892021-11-11 09:13:35-05:000.21104.16145350246427.02.296381e+09100.043.02222222.2203200000.00.546944
51902021-11-11 09:13:46-05:000.11097.97145293312027.02.296381e+09100.044.02222222.2203200000.00.550000
61912021-11-11 09:13:56-05:000.3848.67145414553627.02.296381e+09100.045.02222222.2203200000.00.552778
71922021-11-11 09:14:07-05:000.3834.09145218764827.02.296381e+09100.046.02222222.2203200000.00.555833
81932021-11-11 09:14:17-05:000.2842.17145293721627.02.296381e+09100.047.02222222.2203200000.00.558611
91942021-11-11 09:14:28-05:000.2835.82145308876827.02.296381e+09100.048.02222222.2203200000.00.561667

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
126214472021-11-11 12:51:54-05:000.2914.90198657638433.02.296381e+09100.064.02222222.2203200000.04.185556
126314482021-11-11 12:52:05-05:000.3851.76198760857633.02.296381e+09100.064.02222222.2203200000.04.188611
126414492021-11-11 12:52:15-05:000.2855.41198724403232.02.296381e+09100.064.02222222.2203200000.04.191389
126514502021-11-11 12:52:25-05:000.2827.00198767411234.02.296381e+09100.064.02222222.2203200000.04.194167
126614512021-11-11 12:52:36-05:000.3946.20198810009632.02.296381e+09100.064.02222222.2203200000.04.197222
126714522021-11-11 12:52:46-05:000.21012.97198883328032.02.296381e+09100.067.02222222.2203200000.04.200000
126814532021-11-11 12:52:57-05:000.7994.19199264665632.02.296381e+09100.071.02222222.2203200000.04.203056
126914542021-11-11 12:53:07-05:000.3936.49199081164834.02.296381e+09100.067.02222222.2203200000.04.205833
127014552021-11-11 12:53:18-05:000.3839.48199059456032.02.296381e+09100.067.02222222.2203200000.04.208889
127114562021-11-11 12:53:28-05:000.3940.45198847283233.02.296381e+09100.066.02222222.2203200000.04.211667